Electronic copy available at: http://ssrn.com/abstract=1162852Electronic copy available at: http://ssrn.com/abstract=1162852Electronic copy available at: http://ssrn.com/abstract=1162852Electronic copy available at: http://ssrn.com/abstract=1162852

Target Shooting: Review of Earnings Management around Earnings Benchmarks

Ahsan Habib Department of Accounting

School of Business Auckland University of Technology (AUT)

Private Bag 92006 Auckland 1142 New Zealand

Email: ahsan.habib@aut.ac.nz

James C. Hansen Assistant Professor

University of Illinois at Chicago Department of Accounting (MC 006) College of Business Administration 601 South Morgan Street, 2308 UH

Chicago, IL 60607-7123 Email: jh56@uic.edu

Abstract

In this paper we review the literature dealing with earnings management around earnings benchmarks. The earnings benchmarks are the earnings level (loss avoidance), earnings improvement (earnings changes), and the analyst forecast benchmark. Healy and Wahlen [1999] review the implications of earnings management studies for standard setters. With a standard setter framework and a focus on earnings benchmarks as the motive for earnings management, we document the direction of earning management studies since Healy and Wahlen [1999]. Studies have (1) found that firms’ management have market incentives and also compensation incentives to meet or beat the three earnings benchmarks, (2) questioned the cross-sectional distribution evidence of the frequency of earnings management around the earnings benchmarks, (3) documented methods firms’ management use to manage earnings to beat these benchmarks, and (4) tested whether the market sees through firms that manage earnings to beat benchmarks. We discuss research that identifies factors that constrain earnings management to beat benchmarks and research that has attempted to determine which benchmark is the most important to firms’ management. We also discuss areas of interest to standard setters that have not been addressed by current accounting literature and present avenues for future research.

February 2009

Acknowledgements: Professor Hansen’s contributions are based on a portion of his dissertation at the University of Georgia. He gratefully acknowledges the contributions of his committee: Kenneth Gaver (Chair), Ben Ayers, Linda Bamber, and Jennifer Gaver. We also appreciate the additional comments and review of Peter Easton, Carol Marquardt, Ram Ramakrishnan, and three anonymous reviewers.

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I. INTRODUCTION

Schipper [1989, p. 92] defines earnings management as “purposeful intervention in the

external financial reporting process, with the intent of obtaining some private gain.” Firms

manage earnings because they have some incentive to do so. Healy and Wahlen [1999, p. 368]

state that “earnings management occurs when managers use judgment in financial reporting and

in structuring transactions to alter financial reports to either mislead some stakeholders about the

underlying economic performance of the company or to influence contractual outcomes that

depend on reported accounting numbers.” Burgstahler and Dichev [1997, p.112] state that

“studies of earnings management typically consider a specific incentive for earnings

management (e.g. incentives related to executive bonus plans) and then test whether earnings

have been managed assuming a particular earnings management method (e.g. management of

accruals).”

In this review, we focus on earnings management around three earnings benchmarks: the

earnings level benchmark (loss avoidance), earning changes benchmark (earnings improvement

benchmark), and the analyst forecast benchmark. The earning level benchmark describes

managers who wish to avoid reporting losses and focuses on firms around the zero earnings

level. The earnings changes benchmark describes managers who want to increase earnings as

compared to a prior period and focuses on firms with small positive or small negative earnings

changes. The analyst forecast benchmark describes managers that want to meet or beat analysts’

forecast of earnings and focuses on just missing and meeting or beating the forecast by a few

cents.

Why focus on earnings management around earnings benchmarks? Other papers have

reviewed the earnings management literature [e.g., Schipper, 1989; Dechow and Skinner, 2000;

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McNichols, 2000; Healy and Wahlen, 1999]. The goal of Dechow and Skinner [2000, p. 235-

236] is to reconcile the different views between academics and practioners/regulators. They state

that practioners/regulators feel that earnings management is ‘pervasive and problematic’ and

practioners/regulators find this through experience with ‘specific instances of financial

reporting’. They also state that academics have shown ‘limited evidence of earnings

management’ using studies that focus on large samples and statistical definitions of earnings

management that are not powerful in detecting earnings management. Dechow and Skinner

[2000, p. 236] posit that a ‘fruitful way to identify firms whose managers practice earnings

management is to focus on managerial incentives.’ In regards to these incentives, they state that

academics should ‘focus more on capital market incentives for earnings management’. Dechow

and Skinner [2000, p. 242-245] propose earnings benchmarks as strong capital market incentive

for earnings management.

Healy and Wahlen [1999] review the earnings management literature and discuss its

implications for standard setters. They state [Healy and Wahlen, 1999, p. 367] that standard

setters and regulators are interested in “how much judgment to allow management to exercise in

financial reporting. To help resolve this question, standard setters are likely to be interested in

evidence on (1) the magnitude and frequency of any earnings management, (2) specific accruals

and accounting methods used to manage earnings, (3) motives for earnings management, and (4)

and resource allocation effects in the economy.” They also encourage research on what factors

limit earnings management. In regards to earnings benchmarks, they state that the evidence, from

research concurrent and prior to their review, “does not have direct implications for standard

setters” because these earnings benchmark studies do not address the above four evidences.

[Healy and Wahlen, 1999, p. 379]

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There has been a burst in research that focuses on earnings management around earnings

benchmarks. One important reason for such a burst in benchmark beating research is attributed

to the fact that this type of earnings management research has been supported empirically as will

be evident from the ensuing review. Our goal in this review is to summarize the findings of this

research to see if academics have moved research forward to (1) help standard setters and (2)

bridge the gap between academics and regulators/practitioners. We use the four evidences of

interest to standard setters, listed in Healy and Wahlen [1999], as a framework for this review as

well as the additional consideration of constraints on benchmark beating behavior.

Watts and Zimmerman [1990] review the positive accounting theory throughout the

1980’s. They posit that firms accounting choices are motivated by incentives proxied by bonus

plans, debt covenants, and political costs. This review fits into the positive accounting theory

framework. In Section II, we discuss the incentives or motives firms have to manage earnings to

beat an earnings benchmark. There has developed a large literature that documents firms’

incentives for beating one of the benchmarks [i.e. Barth et al., 1999; Myers and Skinner, 1999;

Bartov et al., 2002; Kasznik and McNichols, 2002].

Section III addresses the frequency of earnings management around benchmarks. Hayn

[1995], Burgstahler and Dichev [1997], and Degeorge et al. [1999] are a few examples of studies

that use a cross-sectional distribution approach to provide evidence of the frequency of earnings

management around earnings benchmarks. Recent research has questioned the validity of the

distribution approaches and we discuss these results and implications for future research.1

1 Durtschi and Easton [2005] suggest that scaling rather than opportunistic earnings management causes discontinuity around zero earnings. They show that the price-per-share is smaller for loss firms than it is for profit firms. The smaller (larger) denominator for loss (profit) firms will drive small loss (profit) firms away from (closer to) the earnings level benchmark. Even with the findings of Durtschi and Easton [2005], there continues to be distributional evidence of earnings management. For example, Degeorge et al. [1999] recognize a scaling problem and use EPS in their study, and find a break in the distribution. We discuss the results of Durtschi and Easton

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Although the cross sectional distribution approach encourages research that examines

earnings management around the earnings benchmarks, McNichols [2000, p. 337] states that “the

distribution approach per se is silent on the approach applied to manipulate earnings.” In Section

IV, we include studies from the late 1990’s through 2008 that have addressed specific accruals

and methods firms are using to manage earnings to beat benchmarks. In Section V, we discuss

resource allocation effects and review whether the market sees through earnings management to

beat benchmarks. In Section VI, we discuss factors that limit earnings management to meet

benchmarks.

Degeorge et al. [1999] examine which benchmark is most important for firms to meet.

They conclude that meeting the earnings level benchmark is the most important, followed by the

earnings changes benchmark, and finally the analyst forecast benchmark. Recent research

questions the validity of this hierarchy and we review this literature in Section VII.

The remainder of the paper proceeds as follows. In Section II we present the incentives

or motives firm management has to beat benchmarks. In Section III we report cross-sectional

distribution evidence of earnings management around benchmarks. In Section IV we discuss

methods of earnings management around benchmarks. In Section V we discuss whether the

market sees through earnings management to achieve benchmarks. In Section VI we discuss the

empirical literature that examines constraining factors on earnings management to meet

benchmarks. Finally, Section VII presents findings on which benchmark is the most important

for firms’ management and provides ideas for future research, summarizes, and concludes the

paper.2

[2005] further in Section III of the paper, and their results highlight the need to be careful when considering the break in the distribution as ‘ipso facto’ evidence of earnings management. 2 One caveat is that even though we try to be exhaustive in this literature review, some papers may not be included due to judgment, error, or because working papers are not publicly available.

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II. INCENTIVES OR MOTIVES FOR FIRMS TO BEAT BENCHMARKS

Standard setters and regulators are interested in motives for earnings management so they

can know where to address standards. External auditors are also interested in these motives so

they know where to direct added audit attention.

A – Underlying Theory

Accounting earnings are viewed as the premier information item provided in financial

statements [Lev, 1989]. Beginning with the seminal work of Ball and Brown [1968] and Beaver

[1968], the last four decades of accounting research have produced a substantial volume of work

showing that the market reacts positively to positive earnings news [see Kothari, 2001 for a

review]. Managers are, therefore, concerned about reporting an earnings number that meets or

exceeds market expectations.3

Earnings are widely used as a key performance indicator of business success and are on

the top of the list of managerial goals [Graham et al., 2005]. A recent comprehensive survey of

Chief Financial Officers (CFO) by Graham et al. [2005] show the GAAP earnings number,

especially the earnings per share (EPS), is the key metric upon which the market focuses. This is

mainly because investors need a simple benchmark to evaluate a firm’s performance, which

reduces the costs of information processing due to the availability of abundant information

[Graham et al., 2005, p. 21]. Academic research is replete with evidence documenting the

primacy of accounting earnings in equity valuation, debt contracting, managerial compensation

contracts and so forth [Kothari, 2001; Bushman and Smith, 2001]. The importance attached to

3 Failure to meet or beat market expectations results in adverse consequences for the firm. For example, Skinner and Sloan [2002, p. 299] find that growth firms missing analysts’ forecast by 0.5% of stock price suffer a significantly negative abnormal return of -10% to -15%. Using survey-based evidence, Graham et al. [2005] also report that missing earnings benchmarks leads to increased market scrutiny of the reported earnings number, increased possibility of lawsuits, additional time and effort required to justify failure, and a general perception among stakeholders about problems in the firm.

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earnings, and the assumption that investors rely on simple heuristics suggest that reporting

earnings that are positive, greater than last year, and greater than the consensus analyst forecast

all have positive valuation implications.4,5

The question then is why investors rely on simple heuristics?6 Kahneman and Tversky’s

[1979] ‘prospect theory’ developed in the field of psychology has been put forward as a plausible

explanation for such an apparently irrational behavior. From a psychological perspective,

prospect theory postulates that decision-makers derive value from gains and losses with respect

to a reference point, rather than from absolute levels of wealth. Furthermore, individuals’ value

functions are convex in losses and concave in gains. This captures the notion that losses are

more displeasing than the equivalent gain. Thus, individuals derive the highest value when

wealth moves from a loss to a gain relative to reference points.7 These features of prospect

theory, therefore, suggest that, ceteris paribus, investors will prefer to invest in companies that

report a series of small gains rather than companies with volatile earnings [Koonce and Mercer,

2005, p. 191].

Another theoretical explanation for managerial incentives to meet or just beat the

benchmark is the transaction cost theory [Burgstahler and Dichev, 1997]. Many ongoing

4 Degeorge et al. [1999] find that in terms of thresholds, avoiding losses and avoiding earnings decreases seem to be

the most important thresholds to achieve. Brown and Caylor [2005], however, find that early in their study period (1985-1993), rank ordering is the same as suggested by prior research, i.e., (a) avoid losses, (b) avoid earnings decreases, and (iii) avoid negative earnings surprises. However, late in their study period (1996-2001), they find managers are more likely to avoid negative earnings surprises than to avoid losses and earnings decreases. They attribute this finding to the markets’ increasing focus on meeting analysts’ expectations that correspond to their study period (1996-2001). Brown [2001] document a temporal shift in reported earnings from (a) failure to meet analysts’ estimates (1984-1990 sample period), (b) meeting analysts’ estimates exactly (1991-1993 period), to (c) beating analysts’ estimates (1994-1999 period). 5Pinnuck and Lillis [2007] argue that reporting losses act as a heuristic trigger for firms to exercise abandonment option and cut down unproductive investments. The authors find that investment in labor is significantly reduced when firms move from a small profit to a small loss zone. 6 See Degeorge et al. [1999, p. 5] for an additional discussion of why thresholds are important. 7 Three commonly mentioned natural reference points used by investors are (a) zero earnings; (b) earnings of the prior year; and (c) the consensus analyst forecast number.

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relations between the firm and its stakeholders remain implicit and generally have no legal

standing. Stakeholders are likely to use multiple sources of information (including past and

current performance on implicit claims) to help assess a firm’s ability to fulfill the implicit

claims. Theoretical arguments suggest that stakeholders are also likely to be influenced by the

firm’s financial image because long-run financial conditions affect the firm’s incentives to fulfill

its implicit commitments. Because the payoffs to the stakeholders are uncertain, the value of the

implicit claims will be materially affected by a firm’s financial condition [Cornell and Shapiro,

1987]. Bowen et al. [1995] show that a proxy for ongoing implicit claims between a firm and its

customers, suppliers, employees and short term creditors creates incentives for managers to

chose long-run income-increasing accounting choices.8 But why should stakeholders use simple

heuristics to evaluate managerial performance? Burgstahler and Dichev [1997, p. 122] suggest

that because, “…the costs of storing, retrieving, and processing information are sufficiently high

that at least some stakeholders determine the terms of transactions with the firm based on

heuristic cutoffs at zero levels or zero changes in earnings”.9

Because prospect theory hypothesizes that individuals derive the highest value when

wealth moves from a loss to a gain relative to reference points, reporting earnings that meet or

exceed these reference points is expected to result in a higher valuation premium. Academic

research is generally consistent with this proposition and is highlighted in the remainder of this

section.

8 Matsumoto [2002] find that firms with greater reliance on implicit claims with their stakeholders, higher transient institutional ownership, and greater value-relevance of earnings exhibit a greater propensity to avoid negative earnings surprises. 9 However, transaction cost theory is premised on the notion of prospect theory, but extends the application to stakeholders with implicit claims on corporate resources.

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Given the significant potential benefits associated with meeting or beating earnings

benchmarks, managers are not passive in the earnings game. Rather they actively try to win the

game by altering reported earnings and/or influencing analysts’ expectations. Meeting

benchmarks boosts managements’ credibility in being able to meet stakeholder expectations and

avoid costly litigation costs that could be triggered by unfavourable earnings surprises [Bartov et

al., 2002]. Academics and regulators tend to interpret earnings management activities around

thresholds as driven by managers’ opportunistic incentives. Regulators seem to believe that

earnings management to meet earnings thresholds deceives shareholders and therefore results in

misvaluation of stocks [Dechow and Skinner, 2000]. However, Arya et al. [2003, p. 111] argue

that managed earnings is not necessarily an evil rather, within limits it promotes efficient

decisions. Moreover, earnings management is a useful device to communicate private

information to owners because such practice reduces owner intervention [Arya et al., 1998].

This proposition is premised on the assumption of Revelation Principle (RP) [Dye, 1988]. This

principle states that “…any equilibrium outcome of any mechanism, however complex, can be

replicated by truth-telling equilibrium outcome of a mechanism under which the agents are asked

to report their private information to the principal…Hence, when the RP holds, the performance

of any mechanism under which managers manipulate earnings can be replicated by a mechanism

under which managers report earnings truthfully” [Arya et al., 1998, p. 7].

Guttman et al. [2006] propose a rational model in which kinks in reported earnings are

endogenously derived, even though both the distribution of true earnings and managerial

compensation schemes are smooth. One of the obvious reasons for such a discontinuity could

emerge from discontinuity in management compensation plans. However, there is no evidence

to suggest that managers are explicitly paid a bonus conditional upon exactly meeting analyst

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forecasts or avoiding losses. Therefore, the kinks in the distribution of earnings are likely to be

caused by other factors like “self-fulfilling market expectations accompanied by a pooling

behavior by managers, whose compensation is tied to the stock price, manifest themselves as

endogenous kinks in the distribution of reported earnings” [Guttman et al., 2006, p. 814].

Another rational theoretical framework for explaining consensus beating phenomenon is

provided by Liu and Yao [2003]. The authors argue that companies are faced with different

growth opportunities. A low growth company may pretend to be high growth to obtain higher

market valuation. As an equilibrium strategy, these firms may exaggerate their reported

earnings. But high growth firms always provide accurate forecasts and meet/beat consensus. In

their model, active earnings guidance is a valuable tool for channeling private information to the

marketplace. Their empirical analysis reveals that consensus-beating companies have (1) higher

market valuation; (2) higher earnings; and (3) less exaggerated and more accurate earnings

forecasts.

These rational models explaining kinks in reported earnings distributions consider

benchmark beating as a signaling mechanism [Fedyk, 2007]. For example, Xue [2005]

hypothesizes that firms without sufficient future earnings do not benefit from earnings

management to exceed thresholds. Given that earnings management through accruals is

reversible, successively meeting thresholds is unlikely if firms do not expect superior future

earnings growth. Earnings management, therefore, conveys managers’ private information about

future firm prospects and reduces information asymmetry between managers and shareholders.

Gunny [2007] provides evidence that firm-years associated with managers that engage in real

activities manipulation (hereafter RAM) to meet an earnings benchmark have higher subsequent

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firm performance. In this situation, using RAM to influence the output of the accounting system

is not opportunistic but consistent with shareholder value maximization.10

As the following review will demonstrate, a majority of the academic research on

benchmark beating considers an opportunistic earnings management perspective and gives less

consideration to a signaling view of beating benchmarks.

B – Capital Market Incentives

Graham et al. [2005] survey 312 financial executives from public companies. They ask

executives which earnings benchmarks are important to them and find that 65.2%, 73.5%, and

85.1% of the respondents agree that the earnings level, analyst forecast, and earnings

improvement benchmarks are important, respectively [Graham et al., 2005, Table 3]. Of the

executives surveyed, 86.3% and 82.2% agreed that meeting earnings benchmarks helped them to

‘build credibility with the capital market’ and ‘maintain or increase stock price’ [Graham et al.,

2005, Table 4], respectively.

DeAngelo et al. [1996] report that firms that have an annual earnings decline after nine or

more years of annual earnings increases have abnormal returns of -14% in the decline year.

Similarly, Barth et al. [1999] find that firms with consecutive years of earnings increases have

higher price-earnings multiples11 than firms without consecutive increases. They also find that

price-earnings multiples decrease significantly when earnings first decline after a period of

consecutive increases. Myers et al. [2007] find similar results using consecutive quarters of

earnings increases.

10 See Xu et al. [2007] for a review of the real activities manipulation (RAM) literature. 11 Barth et al. [1999] define earnings multiple as either the coefficient on net income where price is the dependent variable and net income is an independent variable or the coefficient on change in earnings when returns is the dependent variable and change in earnings is an independent variable.

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Kasznik and McNichols [2002] find that firms that meet or beat analysts’ forecasts in the

current year have higher abnormal returns than firms that do not. They also show that firms that

have met or beat analysts’ forecasts in the current and preceding two years have a market

premium. Mikhail et al. [2004] find that firms that have repeated large positive or negative

earnings surprises have high cost of equity capital, but the costs are higher for firms with

negative earnings surprises.

Brown and Caylor [2005] examine quarterly earnings information. They run regressions

with 3-day cumulative abnormal returns on the earnings announcement date as the dependent

variable, and use dummy variables for the eight combinations of whether firms met or did not

meet the three benchmarks. They find that firms meeting or beating at least one or any

combination of the three benchmarks have a positive valuation consequence, as compared to

firms that meet none of the three benchmarks.12

This evidence generally supports that investors pay a premium for firms that beat the

benchmarks (or receive a penalty for holding firms that miss a benchmark). The evidence is

mixed on whether this behavior is rational. Xue [2005] examines the earnings level and earnings

improvement benchmark. She hypothesizes and finds that firms with high levels of information

asymmetry that appear to manage earnings to beat the earnings level benchmark, do so to signal

to the market that they will be able to sustain the performance in future periods. Abnormal

returns for these firms are positive and significant as compared to firms with lower information

asymmetry and firms that just missed the earnings level benchmark. Results are not consistent

with these same expectations for the earnings changes benchmark.

12 These results need to be interpreted carefully in regards to meeting, just beating, and just missing benchmarks. This is discussed in Section II, Subsection E.

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For investors of firms that beat the earnings forecast benchmark, this behavior appears to

be rational. Bartov et al. [2002] show that firms that meet or beat the earnings forecast

benchmark have higher performance in year t+1 than firms that do not beat the benchmark.

Kasznik and McNichols [2002] find that firms have higher realized earnings in the three years

following meeting or beating an earnings forecast, as compared to firms that miss the forecast.

Although, for the earnings level benchmark, Dechow et al. [2003] find no difference in the future

performance (one-year-ahead market adjusted returns) of small loss and small profit firms.

C – CEO/Upper Management Compensation

A growing body of ‘benchmark beating’ literature examines stock-based compensation

schemes as an important incentive for meeting or beating earnings benchmarks. A substantial

body of theoretical work, beginning with Jensen and Meckling [1976], shows that stock-based

compensation plans can be effective in aligning the incentives of managers with shareholders

and reduce agency costs [e.g., Brickley et al., 1985; Hanlon et al., 2003].13 Equity incentives and

stock-based compensation are important features of the contracting environment between

executives and shareholders which is evidenced by a significant increase in the use of stock

options as a form of executive compensation during the 1990s [Murphy, 1999]. Managers,

therefore, might have various forms of equity-based holdings like unexercisable options,

exercisable options and stock ownership at any point in time. Due to these significant equity-

based holdings, managers’ wealth becomes sensitive to their firm’s stock prices. From the

perspective of risk diversification, such sensitivity may expose risk-averse managers to

idiosyncratic risk of their firm. To reduce such risk exposures, managers are likely to sell shares

they already own. This can motivate managers to increase short-term stock price by

13 Literature on executive compensation is voluminous. For representative surveys and discussion on executive compensation with an emphasis on equity-based compensation, see Core et al. [2003] and Bushman and Smith [2001].

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manipulating earnings [Cheng and Warfield, 2005]. With capital markets focusing on analysts’

EPS targets, which may affect short-term stock price movement, managers try to avoid negative

earnings surprises by reporting earnings figures that meet or beat the analysts’ forecasts.

Accomplishing this may require earnings manipulation if positive earnings innovations due to

real events fail to meet or beat analysts’ forecasts.

Cheng and Warfield [2005] examine the association between equity incentives (proxied

by option grants, unexercisable options, exercisable options, stock grants and stock ownership)

and earnings management (proxied by meeting and or beating analyst forecasts). The authors

report that firms with high equity incentives are more likely to meet or just beat analysts’

forecasts. This finding could imply either an opportunistic or signaling view of earnings

management. For example, firms with superior performance are more likely to meet and or beat

analyst forecasts and this better performance may be responsible for higher stock prices rather

than earnings manipulation driving short-term stock prices. McVay et al. [2006] conduct a

similar analysis and find that subsequent managerial sales are significantly positively related to

the likelihood of just meeting the analysts’ forecasts. This result may imply that managerial

sales are passive responses to good firm performance. Alternative tests to rule out such a

possibility show that (a) managers appear to manage working capital accruals prior to just

meeting the threshold and trading; (b) this relationship does not hold for non-manager insiders,

who are unlikely to have the power to manage earnings at their discretion; and (c) this strategic

behavior is weakened in the presence of an independent board of directors, suggesting that good

corporate governance mitigates this strategic behavior.

Matsunaga and Park [2001] test whether beating the three earnings benchmarks effects a

CEO’s cash compensation. Their results suggest that CEO bonus payments give CEOs an

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economic incentive to beat the analyst forecast benchmark and the earnings changes benchmark.

They do not find evidence of a relationship between CEO bonus payments and loss quarters.

This evidence is consistent with Gaver and Gaver [1998], which shows that gains flow through

to compensation, but losses do not. Adut et al. [2003] look specifically at restructuring charges

and show that compensations committees examine characteristics of each restructuring charge

before deciding whether to shield executives’ compensation from the charge. Adut et al. [2003]

provide evidence that, under certain circumstances, even restructuring charge losses generated by

firms can affect CEO compensation. Future research could examine whether compensation

committees evaluate characteristics of small losses and whether certain characteristics do indeed

affect CEO compensation. Adut et al. [2003] compare first time restructuring charge losses to

recurring restructuring charge losses. This could be applied to the small loss setting; a

comparison of recurring versus nonrecurring charges and whether they flow through to

compensation.

Bauman et al. [2005] examine the roles of income-increasing accounting choices and

management guidance to analysts (forecast guidance) in meeting or beating analysts’ forecasts

for firms employing relatively high levels of stock-based compensation. This study contributes

to the benchmark beating literature by documenting the additional role played by management

guidance to financial analysts. The authors find that firms compensating top managers more

heavily with stock options employ expectations-reducing guidance to financial analysts, not

income-increasing abnormal accruals, to enable them to more frequently meet analysts’ earnings

targets. The result from Bauman et al. [2005] is in contrast to the findings of McVay et al.

[2006] who find that the managers manage working capital accruals but not analysts’ forecasts to

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achieve earnings targets.14 An important factor to consider in deciding between forecast

guidance and earnings management is the expected cost of accruals management.15 Also, when

managers guide forecasts downwards, stock prices are expected to go down because of lower

expected earnings. This is likely to be offset by subsequent expected stock price rise from

meeting the expectation. If managers are uncertain as to whether the upward effect associated

with meeting analyst expectation outweighs the downward impact of guidance, managers are

unlikely to use forecast guidance. Bauman et al. [2005] did not, however, test whether these are

valid propositions driving their result.

D – Debt

Besides equity valuation, accounting data are explicitly used in writing lending contracts.

There is a dearth of research on managerial propensities to engage in benchmark beating

behavior to affect cost of debt capital. Jiang [2008] is the only study that directly tests such a

proposition. Jiang [2008] finds that firms beating earnings benchmarks have (a) better one-year

ahead credit ratings; and (b) a smaller initial bond yield spread. Since the bond market is

dominated by institutional investors who are more sophisticated in processing complex

information than other investors, it is not clear as to why this market should rely on simple

heuristics. However, not all institutional investors are efficient to the same extent in processing

information and ‘transient’ institutional investors [Bushee, 1998] compared to their ‘non-

transient’ counterparts may rely more on simple heuristics to evaluate firm performance. Jiang

[2008] does not test such a possibility and future research could examine this. Jiang [2008] also

14 Bergstresser and Philippon [2006] also report that managers use more income increasing discretionary accruals when there compensation is more closely tied to stock options. 15 Marquardt and Weidman [2004] state that potential costs of accruals management could be categorized into “detected” and “undetected” accruals management. Regulatory enforcement actions, negative market reactions from earnings restatements, shareholder litigation, qualified audit reports, and negative coverage in the business press are some examples of “detected” accruals management. Whereas, accruals reversals, constraints on future reporting flexibilities, increased audit costs to unravel undetected accruals management and perception of poor earnings quality are examples of “undetected” accruals management.

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reports that firms that meet or beat benchmarks by managing earnings do not experience reduced

cost of debt to the same extent as that of non-managing firms. However, including only earnings

management techniques without considering forecast guidance, RAM and classification shifting

to meet or beat benchmarks may fail to capture the complete picture of benchmark beating16.

E – Underlying Function

Figure 1 presents two possible graphs of the function for the relation between the

earnings benchmarks and executive compensation. Executive compensation could be replaced

with abnormal returns. The x-axis represents any of the three earnings benchmark where zero

represents just meeting the benchmark. The graph is a simplification, where the true functions

are most likely asymmetric [e.g. Matsunaga and Park, 2001] and nonlinear [e.g. Freeman and

Tse, 1992; Kinney et al., 2002]. Panel B shows a function where the benchmarks affect

compensation or abnormal returns.

<Insert Figure 1>

The experimental design of many of the earnings benchmark studies cited in this section

use all of the firms above and below the benchmark to show that there is a difference for firms

that meet or beat and miss the benchmarks in compensation or abnormal returns. Thus it is hard

to determine whether the true function underlying the results comes from either the function in

Panel A or Panel B. For example, Brown and Caylor [2005] include firms as beating

benchmarks whether the firms have done so by small or large amounts. Their results could be

interpreted as just a reaction to good news (Panel A). They do not identify the valuation

consequences of just meeting or just beating the benchmarks. Also, Lopez and Rees [2002]

show that missing (beating) the analyst forecast benchmark is significantly associated with

negative (positive) returns, regardless of the magnitude of the forecast error. Future research is

16 Classification shifting is discussed in Table 1, Panel D of the paper.

17

needed to address this issue. One potential way to address this issue is to use a methodology

similar to Ayers et al. [2006], which is discussed more thoroughly in Table 1, Panel B. The

difference between abnormal returns (compensation) just above and just below the benchmarks

of interest can be compared to the difference around pseudo-benchmarks throughout the earnings

distributions. If researchers cannot find that there is a break in the earnings-return (earnings-

compensation) distribution at the benchmark, they may potentially find that the differences in

returns above and below the benchmark are greater than the differences around pseudo-

benchmarks.

Kinney et al. [2002] find that the slope of the returns for firms that just beat and just miss

analysts’ forecast by ± 1 cent are much steeper than the slope above +2 and below –2 cents. For

the analyst forecast benchmark, it is more likely that the returns around zero are likely caused by

non-linearity rather than penalty for losses. Similar tests are needed for the earnings level and

earnings changes benchmarks. Tests are needed for all three benchmarks using executive

compensation.

Additionally, Kinney et al. [2002] conclude that a substantial number of firms with

negative forecast errors have positive returns, and correspondingly, many firms with positive

forecast errors have negative returns. They conclude that the probability of earnings a positive

return based on a trading strategy of selling short a security based on knowledge of whether a

firm missed the analyst forecast benchmark would be less than 66.7 percent. Research is needed

to find if results are similar around the other benchmarks. Also, more research is needed to

identify which firms are driving the overall negative return for firms that miss the benchmarks.

For the analyst forecast benchmark Skinner and Sloan [2002] find that growth firms appear to

drive much of this result.

18

F – Section Summary

In summary, evidence from accounting research is mixed, but generally shows that firms

have capital market incentives to beat all three earnings benchmarks. CEOs (or Upper

Management) have cash and equity-based compensation incentives to beat the earnings

improvement and analyst forecast benchmark. Managers also appear to ‘strategically’ manage

earnings to meet or just beat the analyst forecast benchmark prior to managerial stock sales. The

cost of debt is affected by firms meeting or beating benchmarks. Auditors also recognize the

importance of beating the analyst forecast benchmark for their clients. Future research is needed

to show whether CEOs are shielded from all losses [i.e. Gaver and Gaver, 1998], whether certain

loss characteristics flow through to CEO compensation [i.e. Adut et al., 2003], whether corporate

governance attenuates earnings management around earnings benchmarks, and what the true

function is for the relation between compensation/abnormal returns and missing or beating the

three earnings benchmarks.

III. FREQUENCY AND MAGNITUDE OF EARNINGS MANAGEMENT AROUND

BENCHMARKS

A – Background

Hayn [1995] uses a cross-sectional distribution approach to provide evidence that firms

manage earnings to beat the earnings level benchmark. Hayn [1995] documents that there are

too few firms just below the earnings level benchmark and too many firms just above.

Burgstahler and Dichev [1997] complement and extend her research by showing similar results

for the earnings level benchmark and the earnings changes benchmark. Holland and Ramsay

[2003] and Suda and Shuto [2005] support the external validity of Hayn [1995] and Burgstahler

19

and Dichev [1997] by showing similar results for Australian and Japanese firms, respectively.

Degeorge et al. [1999] and Burgstahler and Eames [2006] examine the analyst forecast

benchmark and find similar cross sectional results. The visual evidence from these studies

suggests that firms focus on these benchmarks and attempt to overcome them.

The firms just above the earnings benchmarks are not all considered to be earnings

managers. Burgstahler and Dichev [1997, p. 101] “estimate that 8-12% of firms with small pre-

managed earnings decreases manipulate earnings to achieve earnings increases, and 30-44% of

firms with small pre-managed losses manage earnings to create positive earnings.” Dechow et

al. [2003] estimate that 85-90% of firms that beat the earnings level benchmark are expected to

be there by chance, and 10-15% are firms that potentially have managed earnings. The

frequency of earnings management around the analyst forecast is hard to quantify. Firms can

manage earnings and also guide analysts’ forecasts to beat the analyst forecast benchmark [e.g.

Matsumoto, 2002].

As stated in Healy and Wahlen [1999], these studies give us evidence about the

frequency but not the magnitude of earnings management.

B – Cross-sectional distribution evidence questioned

Beaver et al. [2007] and Durtschi and Easton [2005] have questioned the validity of the

visual cross-sectional distribution evidence provided by Burgstahler and Dichev [1997] and

others. Burgstahler and Dichev [1997] examine net income scaled by market value of equity for

the earnings levels and earnings changes benchmarks. Beaver et al. [2007] focus on problems in

the numerator, or characteristics of net income that affect the break in the distribution around

benchmarks. Durtschi and Easton [2005] focus on problems in the denominator [i.e.

characteristics of scaling by market value of equity (or other scaling variables) that affect the

20

break in the distribution around benchmarks].17 These studies do not eliminate capital market

incentives for beating these benchmarks, but their findings make it hard to determine the

frequency of earnings management around the benchmarks.

Durtschi and Easton [2005] clearly present that the price-per-share is smaller for loss

firms than it is for profit firms. The smaller (larger) denominator for loss (profit) firms will drive

small loss (profit) firms away from (closer to) the earnings level benchmark. Durtschi and

Easton [2005] suggest that it is this scaling effect that causes the break in distribution of

earnings. They examine the distribution of EPS and change in EPS, distributions that are not

confounded by market-price-scaling. Durtschi and Easton [2005, Figure 1, Panel B, p. 564-565]

find that for the distribution of EPS, there are more firms with -$.01 of EPS than firms with $.01

of EPS. They also find that for the distribution of changes in EPS that there is asymmetry, but

the distribution is drastically different than Burgstahler and Dichev [1997].

Although this evidence is compelling against the cross sectional distribution evidence,

some results in Durtschi and Easton [2005] appear to support the findings of Burgstahler and

Dichev [1997]. Figure 3, Panel B in Durtschi and Easton [2005] contains un-scaled net income

for which there is beginning of the period stock price available on Compustat. There is a break

in this distribution just below zero, although the break is not pronounced as that in Burgstahler

and Dichev [1997]. We take this analysis a step further to emphasize some areas for future

research.

<Insert Figure 2>

We first replicate the results of Durtschi and Easton [2005] for the 1988-2005 time

period. Figure 2, Panel A contains the cross sectional distribution of firms with EPS data on

17 A similar but brief analysis appeared in Dechow et al. [2003, p. 375-378]. Also, the scaling issue is recognized and addressed in Degeorge et al. [1999].

21

Compustat from 1988 – 2005, along with the actual number of firm-year observations at each

level of EPS from -$0.20 to $0.20. Consistent with the findings of Durtschi and Easton [2005]

and Dechow et al. [2003], Figure 2, Panel A shows that there are more firms at -$.01 and -$.02 of

EPS than there are at $.01 and $.02. This evidence is not consistent with Burgstahler and Dichev

[1997].

We next graph the same set of firms, highlighting firms with share price ≥ $1.00 and then

firms with share price < $1.00.18 Both the NYSE and Nasdaq require that listed companies

maintain a $1 bid price or they will be delisted. Figure 2, Panel B contains the same firm-year

observations from Panel A for which share price at the end of the fiscal period is greater than or

equal to $1.00. The break in the distribution is again present. This could be viewed as evidence

that it is more important for firms with share price ≥ $1.00 to have positive earnings than penny-

stock firms. It could also be the case that firms that beat the benchmark and have higher

earnings levels draw a higher stock price. Figure 2, Panel C contains the same firm-year

observations from Panel A for which share price at the end of the fiscal period is less than $1.00.

A large portion of the firm-year observations that lead to the findings of Durtschi and Easton

[2005]—more firms just below the benchmark than just above—are penny stocks. As mentioned

in Section II, Subsection A, Burgstahler and Dichev [1997, p. 121-124] present two theories as

possible explanations for why firms would want to beat the earnings levels and changes

benchmarks—transaction costs and prospect theory. Under either of these theories, penny-stock

firms have the same incentives to meet or beat the benchmarks as firms with stock price ≥ $1.00

(This provides a nice area for future research). If penny stocks have the same incentive under

prospect and transaction costs theory to beat the earning level benchmark, then what keeps these

18 Similar analysis was suggested and presented by Dave Burgstahler in discussant comments given for Durtschi and Easton [2005] at the 2005 American Accounting Association Financial Accounting and Reporting Section Midyear Meeting. A share price of $1.50 was used as the cutoff in his presentation.

22

firms from beating the benchmark? Are these firms in a position where they have never been

profitable? Are these firms moving to profitability? Do these firms have constraints that limit

them from moving to profitability? In a dividend setting, DeAngelo et al. [2006] use retained

earnings as a percentage of total equity or total assets to proxy for where a firm is in their life-

cycle (young vs. mature firms). A similar measure could be tested in a benchmark setting to see

whether firms’ position in their life-cycle affects their ability to beat benchmarks. Barton and

Simko [2002] use net operating assets as a proxy for firms’ ability to manage earnings and test

this around the analyst forecast benchmark. This could be applied to the other benchmarks.

Using these suggestions, further research is needed to help regulators, standard setters, and

academic researchers determine if the break in the distribution is evidence of earnings

management.

Figure 2, Panel D contains the same firm-year observations from Panel A for which $1.50

≥ share price at the end of the fiscal period ≥ $1.00. These firms are close to delisting and this

delisting threshold provides a stronger threshold than the earnings level benchmark alone. As in

Burgstahler and Dichev [1997], there is a break in the distribution just below zero earnings. The

distribution is skewed to the left—there are many firms that are close to delisting that have

losses. As just mentioned, research is needed to explore the characteristics of the firms close to

delisting that reported losses. When proposing new standards that affect reporting, these

characteristics can help standard setters identify firms that do not appear to be managing earnings

even when facing the incentives of beating the earnings level benchmark and avoiding delisting

thresholds.

Beaver et al. [2007] examine characteristics of the numerator, or net income, which affect

the break in the earnings distribution. They show that taxes and special items can explain a large

23

portion of the break in the distribution. Beaver et al. [2007] examine the distribution of earnings

before taxes (operating income) to see how these firm year observations are affected by taxes

(special items) as they move down the income statement to arrive at net income (earnings before

taxes). They posit that if the distribution of net income (earnings before taxes) and earnings

before taxes (operating income) look different, then the nature of taxes (special items) is driving

the break in the distribution. In the case of taxes, profit firms are brought closer to the

benchmark through taxes where loss firms are moved away.

The tax affect over accentuates the break in the distribution. Dhaliwal et al. [2004] find

that firms use income tax expense to meet or beat the analyst forecast benchmark. Beaver et al.

[2007] find that this does not appear to be the case for small profit firms. A majority of these

firms have tax expense that have brought them closer to the zero earnings level. Beaver et al.

[2007] do not perform an analysis of earning changes due to the high correlation of earnings

levels to earnings changes. To answer the question of whether firms use tax expense to beat the

earnings changes benchmarks, researchers may take a sample of firms that are not close to the

earnings level benchmark but are close to the earnings changes benchmark. This could

potentially isolate the effect documented in Beaver et al. [2007] and allow a cleaner test of tax

expense as an earnings management tool to meet the earnings changes benchmark. Beaver et al.

[2007, p. 3] state, “we do not interpret our findings as precluding that firms manage earnings to

avoid losses or earnings declines.” This also presents an area of future research. If firms are

managing earnings to beat the earnings level benchmark, how do they overcome the additional

affect of taxes?

C – Section Summary

24

Durtschi and Easton [2005] present evidence that scaling does have an affect on the

results of Burgstahler and Dichev [1997]. Beaver et al. [2007] find that the earnings before taxes

(operating income) distribution does not match the net income (earnings before taxes)

distribution. They attribute a portion of the break in the distribution of net income to the nature

of taxes and special items. Both Durtschi and Easton [2005] and Beaver et al. [2007] results

provide areas for future research and highlight that researchers need to be careful when using the

break in the distribution as ‘ipso facto’ evidence of the ‘intensity’ or frequency of earnings

management.19

Most research examining firms around the earnings level or changes benchmark scale

earnings similar to Burgstahler and Dichev [1997]. It would be helpful to have a study that

examines methods discussed in Section IV to see if they continue to hold around the earnings

level and changes benchmark where the earnings are scaled by shares outstanding (EPS). A lack

of consistent results would reinforce the scaling issue, whereas consistent results would alleviate

concerns about the scaling methodology used in Burgstahler and Dichev [1997] and many other

studies.

There has been limited research on the magnitude of earnings management to beat

benchmarks. This is due in part to errors in measuring cross-sectional measures of earnings

management (e.g. discretionary accruals—for an in depth discussion see Dechow and Skinner

[2000]). Elgers et al. [2003] and Dechow et al. [2003] find that problems arise when researchers

try to ‘back-out’ earnings management (or measure the magnitude of earnings management)

using discretionary accrual models. Beaver et al. [2003] are able to measure the magnitude of

19 There are additional papers addressing the distributions of earnings. For example, Jacob and Jorgenson [2007] show that for a sample of ‘rolling quarters’ the break in the distribution is the most pronounced for firm-year observations with four quarters ending in the traditional 4th Quarter. Firms with four quarters ending in 1st, 2nd, and 3rd quarters do not show the break in the distribution. Durtschi and Easton [2008] examine issues involved with the Jacob and Jorgenson [2007] paper.

25

earnings management to beat the loss avoidance benchmark in the insurance industry. They do

this by examining a specific account—loss reserves—which is an estimate and is ‘trued up’ over

time. The ‘true up’ of most accounts are not disclosed as specifically as they are with insurance

companies.

In Section IV, we summarize a variety of methods firms use to meet the earnings

benchmarks. The large number of possible methods leads to problems in measuring the

magnitude of earnings management. As pointed out in Section III, Subsection A, in tests of

earnings management around the earnings level benchmark there appear to be 44 percent or

fewer firms above the benchmark that are managing earnings. Researchers need to sort through

the potential earnings management methods to identify the potential earnings managers and to

quantify the magnitude of earnings management. The Securities and Exchange Commission

(SEC) Accounting and Auditing Enforcement Releases (AAER) focus on firms that are known to

have committed violations of SEC guidelines and federal security statutes. The SEC does not

use cross-sectional measures (such as discretionary accruals) to identify violators.20 Accounting

researchers need to bring their knowledge of earnings management methods together to help

regulators with better predictive models.21 We do not have the solution for overcoming this

problem, but until researchers come up with better predictive models, all we are documenting are

descriptive characteristics that are statistically significant around the earnings benchmarks.

20 They might if the measure were more reliable. 21 Dechow et al. [2007] examine SEC Accounting and Auditing Enforcement Releases from 1982-2005 and highlight balance sheet items that identify firms that are known to have manipulated earnings. Dechow et al. [2007] attempt to come up with a prediction models for the AAER set.

26

IV. METHODS OF EARNINGS MANAGEMENT TO BEAT BENCHMARKS

Healy and Wahlen [1999, p. 379] discuss tests of distributions of reported earnings in a

section of their review. They summarize this section and state that these tests provide

‘convincing evidence’ that some firms manage earnings when they anticipate missing an

earnings benchmark, but that the evidence does not have a direct implication for standard setters.

Healy and Wahlen [1999, p. 379] state that “what is currently lacking from these studies is a

clear understanding of the steps that these firms take to increase reported earnings, the magnitude

of earnings management, the effect of this type of earnings management on resource allocation,

and whether such earnings management can be mitigated by additional standards.” Table 1

outlines methods firms take to increase reported earnings around benchmarks.

<Insert Table 1 Here>

As shown in Table 1, recent research suggests that there are many ways for firms to

manage earnings to meet or beat benchmarks. The options range from manipulating real

activities (e.g. R&D expenditures) to manipulating aggregate accruals. With the plethora of

methods used to beat benchmarks, it should be no surprise that studies using aggregate measures

of earnings management (e.g. discretionary accruals) sometimes do not find results [e.g. Dechow

et al., 2003]. Future research needs to aggregate these individual methods to better describe

earnings management around earnings benchmarks. Research is needed to examine 1) which

firms use specific methods of earnings management to beat benchmarks and 2) why firms prefer

one earning management method to another.22 This will aid standard setters in identifying which

benchmark-beating firms are managing earnings and which fall there through normal operations.

22 We thank an anonymous reviewer for pointing this out.

27

To accomplish this, researchers might compare firms with the highest levels of each of

the different earnings management methods above the benchmarks to see if it is the same set or if

it is unique sets of firms. Then researchers could identify if the set of firms with high levels of

the measure just above are different from firms just below the benchmark. Hansen [2008] finds

that firms below benchmarks respond to incentives or benchmarks other than the benchmark of

interest. If not controlled for, these other incentives or benchmarks may confound efforts to

identify the earnings management method of choice. Another consideration is to examine firms

that have strong incentives to beat multiple benchmarks to see if they exhibit higher levels of

specific earnings management measures.

As a side note, the SEC has limited resources to pursue the many leads they have in

regard to potential violators of accounting rules. The SEC chooses the cases with the highest

likelihood of success. It would be nice if academics and regulators could cooperate to examine

the firms that the SEC does not pursue. There may be fruitful information that can be garnered

from this set of firms.

V. DOES THE MARKET SEE THROUGH EARNINGS MANAGEMENT TO BEAT

BENCHMARKS?

In this section we discuss resource allocation effects from firms’ management managing

earnings around benchmarks. Do firms that manage earnings have rewards for meeting and

beating earnings benchmarks or do capital markets see through the earnings management?

Gleason and Mills [2008] follow up the study by Dhaliwal et al. [2004] to see if the market

reward to meeting the analyst forecast benchmark is affected when firms’ management use tax

expense to manage earnings. The authors compare firms that meet or beat the analyst forecast

28

benchmark using tax expense management to those that meet or beat using no earnings

management. The authors find that the reaction (measured using cumulative size-adjusted

returns) for firms using tax expense management to meet or beat the forecast is positive but

smaller than firms that meet or beat the analyst forecast without tax expense management. Firms

that meet or beat the forecast had more positive reactions than firms that missed the forecast,

regardless of whether tax expense management was involved. These findings are interesting

because the market appears to see through the earnings management but does not fully discount

for the tax expense earnings management.

Similarly, Bartov et al. [2002] show that firms that meet or beat analysts’ earnings

forecast in the current quarter have higher returns than firms that fail to meet or beat. They show

that although the premium is smaller, it still exists even when firms likely meet or beat forecasts

either through earnings management or through managing expectations.

Bhojraj et al. [2003] define firms with high earnings quality as firms with high research

and development expenditures, high advertising expenditures, and low total accruals. They find

that firms that beat the analyst forecast benchmark and have low quality earnings have higher

one-year size adjusted returns than firms that missed the analyst forecast benchmark and have

high quality earnings. Interestingly, firms that miss the analyst forecast benchmark and have

high quality earnings have higher two-year and three-year cumulative size adjusted returns than

firms that beat the analyst forecast and have low quality earnings. The results of Bhojraj et al.

[2003] suggest that managing earnings to beat the analyst forecast will give firms benefit in the

short run, but not over a longer horizon.

The evidence in Gleason and Mills [2008], Bartov et al. [2002], and Bhojraj et al. [2003]

suggests that firms receive market rewards in the short run for beating the analysts' forecast, even

29

when these firms manage earnings. Additional research is needed to examine resource allocation

effects for meeting the earnings level and the earnings changes benchmark. Similar to previous

research suggestions, it is important to identify firms just above the earnings level and earnings

changes benchmarks which are earnings management candidates so market reward tests can be

carried out.

VI CONSTRAINTS ON BENCHMARK BEATING BEHAVIOR

A – Constraints Literature

The survey evidence summarized so far indicates the existence of earnings management

to avoid losses, increase reported earnings and meet or beat analysts’ forecasts. Earnings

management literature has suggested that these actions are undertaken to mislead investors and

could result in resource misallocation [Lin et al., 2006; Athanasakou et al., 2008]. Because

resource misallocation is costly, it is important to understand factors that are likely to constrain

earnings management relative to thresholds.

Barton and Simko [2002] hypothesize and find evidence that managers’ ability to

optimistically bias earnings to meet a threshold decreases with the extent to which net operating

assets (hereafter NOA) are already overstated in the balance sheet. However, DeFond [2002]

criticizes Barton and Simko [2002], arguing that the study suffers from a lack of coherence

between the conceptual definition of net assets overstatement and empirical constructs to

measure such overstatement. Furthermore, the proxy used by Barton and Simko [2002] could

also be interpreted as a performance measure and may confound the result reported by the

authors.

30

Following Barton and Simko [2002], Smith [2004] examines whether investors use

balance sheet information to differentiate ex-ante constrained (overstated NOA) from ex-ante

flexible (understated NOA) firms. Investors’ response to positive earnings surprises of

constrained firms is likely to be greater than that of the flexible firms because the former could

only report positive earnings surprises by taking actions that affect the real operating

performance. However, flexible firms have the choice of managing earnings to report such a

positive surprise. Smith [2004] finds evidence consistent with this proposition. The reported

result needs to be carefully considered because not all flexible firms will choose to manage

earnings given the cross-sectional variation among the flexible firms regarding earnings

management incentives.

In addition to GAAP-based constraints on beating benchmarks, a number of empirical

works examine the role of corporate governance mechanisms in curbing this behavior. The

traditional agency theory arguments for corporate governance focus on the information

asymmetry problem between managers and shareholders [Jensen and Meckling, 1976]. This

information asymmetry creates incentives for corporate managers to engage in dysfunctional

behavior to maximize short-term wealth at the expense of long-run value creation [Schipper,

1989]. Boards of directors are widely believed to play an important role in monitoring top

management [Fama and Jensen, 1983]. Effective corporate governance requires that the majority

of the board members should be independent of corporate management. The primary benefit of

having a board of directors composed of a majority of outside directors is that they can

objectively evaluate managerial performance and constrain earnings management behavior.

Peasnell et al. [2005] find in the UK, when pre-managed earnings are negative or below last

year’s reported earnings, abnormal working capital accruals are less positive if the non-executive

31

director (NED) ratio is relatively high. However, this occurs only in the case of income-

increasing earnings management. In the case of income-decreasing earnings management, the

authors fail to find any constraining role of NEDs.

In an earlier study, Peasnell et al. [2000] examine whether the association between board

composition and earnings management activity differs between the pre- and post-Cadbury

Report period.23 They find that in the post-Cadbury Report period (1994-1995), managers

engage in less income-increasing accruals to avoid reporting losses and earnings declines, when

the proportion of NED is high. No such association is found in the pre-Cadbury Report period.

However, this could simply reflect the reduced incentives for earnings management by UK

managers as reflected in the lower leverage ratio in the post-Cadbury Report period. The result

remains unchanged after controlling for the leverage effect. Considering only leverage ratio,

however, is insufficient because of the presence of other incentives such as stock-based

managerial compensation, growth opportunities, capital market transactions, etc., in managing

earnings to achieve thresholds. Park and Shin [2004] investigate the association between board

independence and benchmark beating in Canada. Canada has a well-developed equity market

but unlike the US and the UK, many Canadian firms are controlled by a large blockholder

(concentrated ownership regime). Controlling blockholders in these firms can expropriate

minority shareholders’ wealth by engaging in manipulative accounting practices. Independent

boards may constrain such practices by performing a monitoring function over corporate

managers. Park and Shin [2004] find support for this hypothesis but only for directors who are

officers of financial intermediaries, due probably to their sophisticated financial skills. They also

23 The Cadbury Report was published in December 1992 and contains a Code of Best Practice designed to serve as the benchmark against which good governance can be assessed. The Code recommends, inter alia, that all firms create an audit committee with at least three members and consisting exclusively of NEDs. While the Code does not explicitly specify a minimum number of non-executive board members, the recommendation relating to audit committees means that firms must have at least three NEDs to report full compliance [Peasnell et al., 2000].

32

find some evidence that board representations of large pension funds reduce earnings

management further. Davidson et al. [2005] find that the existence of an audit committee is

negatively related to small changes in earnings in Australia. However, neither board

independence nor audit committee independence is associated with small increases in earnings.

Another important governance mechanism is the external auditor. Auditors are aware of

the incentives firms have to beat the analyst forecast benchmark [Libby and Kinney, 2000].24

There is contradictory evidence regarding the role of external auditors in constraining earnings

management. Frankel et al. [2002] report a positive relationship between non-audit fees

(hereafter NAF) and the propensity to generate small earnings surprises. Ashbaugh et al. [2003],

however, call into question the Frankel et al. [2002] findings by arguing that the dependent

variable (fee ratio) used by Frankel et al. [2002] does not distinguish between economically

significant and benign firms. When Ashbaugh et al. [2003] use total fee (sum of audit and non-

audit fees) instead of fee ratio, the positive relationship between NAF and small earnings

surprises documented by Frankel et al. [2002] disappears. Lim and Tan [2008] argue that higher

levels of NAF do not necessarily imply low quality audit if such a high level of NAF is paid to

industry specialist auditors. The authors report that firms audited by industry specialist auditors

are significantly more likely to miss analysts’ forecasts when NAF increases. Francis et al.

[2006] extend Lim and Tan [2008] by categorizing industry specialists into (1) both a city and a

national leader; (2) a city-specific leader but not a national leader; (3) a national leader alone.

Francis et al. [2006] report that firms are less likely to meet or just beat analyst earnings forecasts

24 Libby and Kinney [2000] run an experiment where auditors are asked how much they expect an immaterial, misstated accounting estimate to be adjusted for an average client. They find that their auditor subjects expect an adjustment if the adjustment would not cause a company to miss analysts’ forecast. Through this experiment, auditors acknowledge the importance of beating analysts’ forecasts for their clients. A similar experiment is needed to determine if auditors also recognize the importance of the earnings level and earnings changes benchmarks for their clients. One difficulty with this type of experiment is the change from the pre- to post-Sarbanes-Oxley environment. Meeting or beating benchmarks may be more salient to auditors in the post-Sarbanes-Oxley environment.

33

when the auditor is either a city-specific or both a city and national leader. However, national

industry leaders alone have no association with meeting or just beating forecasts. This implies

that differential auditor industry expertise is primarily driven by city-specific audit expertise

because of the localized nature of such expertise.

The role of institutional investors in corporate governance yields mixed evidence.

Institutional investors can either encourage myopic managerial behavior [e.g., Bhide, 1993;

Froot et al., 1992] or actively participate in firm monitoring [e.g., Bushee, 1998; Bange and

DeBondt, 1998]. Koh [2007] finds support for the “efficient monitoring” hypothesis in that

long-term institutional investors (dedicated investors) constrain accruals management among

firms that manage earnings to meet or beat thresholds.

Thomas et al. [2004] explore the Japanese setting where both parent and consolidated

financial statements are prepared to examine whether firms engage in earnings management

using transactions with affiliated companies. They find earnings management behavior around

three earnings thresholds for both parent and consolidated earnings. Further, the distributions for

parent earnings show substantially more evidence of earnings being managed at these thresholds

consistent with the parent using its dominant position over its affiliates to structure transactions

in such a way that it increases the reported profit of the parent without affecting the group’s

earnings result.

B - Section Summary

With respect to constraints on benchmark beating behavior, evidence suggests that

corporate governance variables, like an independent board, institutional investors and external

auditors play a particularly significant role in constraining such behavior. Academic research on

the effect of corporate governance on managerial propensity to meet and or beat earnings

34

benchmarks, however, needs to be carefully evaluated in light of proper measurement of

corporate governance variables. Larcker et al. [2007] find that a comprehensive set of different

governance measures explains only 0.6% to 5.1% of the cross-sectional variation of their

dependent variables (e.g., abnormal accruals, Tobin’s Q, accounting restatements). They

attribute the failure to find any consistent relationship between corporate governance measures

and organizational performance to the difficulty in generating reliable and valid measures for the

corporate governance construct. Brown and Caylor [2006], on the other hand, find support for

the positive role of corporate governance measures in value-creation based on a composite

governance measure of 52 variables. Failure to incorporate a relevant governance measure, like

executive and director remuneration, may result in a correlated-omitted variables problem and

lead to erroneous conclusions. Another potential question for future research is the relative

importance of GAAP-based earnings management constraints such as NOA [Barton and Simko

2002].

VII. FUTURE RESEARCH, SUMMARY AND CONCLUSION

A – Benchmark Importance

Degeorge et al. [1999] examine the three earnings benchmark distributions conditional

upon meeting or missing the other two benchmarks to determine which benchmark is the most

important for firms. Their tests place the earnings level benchmark as the most important,

followed by earnings changes, and finally, analyst forecast benchmark. Dechow et al. [2003]

examine the kink in the cross sectional distribution of firms, to see whether the kink is changing

throughout time. They find that the kink is declining for the earnings level and earnings changes

distribution, but increasing for the analyst forecast benchmark. They provide this as initial

35

evidence that the hierarchy of benchmark importance is shifting from the earnings level to the

analyst forecast benchmark.

Recent evidence shows that the importance of meeting the analyst forecast benchmark

has increased in recent years [Brown, 2001; Bartov et al., 2002; Lopez and Rees, 2002;

Matsumoto, 2002]. Brown and Caylor [2005] further explore the hierarchy of earnings

benchmarks using data from 1985-2002. Similar to Burgstahler and Dichev [1997], Brown and

Caylor [2005] calculate a standardized difference for the group of firms just below a benchmark

(actual numbers of observations in the interval just below a benchmark minus the expected

number of observations divided by an estimate of the standard deviation of the difference). The

benchmark with the most negative standardized difference is regarded as the most important

benchmark for firms to beat. Brown and Caylor [2005] run regressions of the standardized

difference on year to see how the importance of each benchmark has changed over time. They

find that from 1985-1993 the earnings changes benchmark is the most important, followed by the

earnings level benchmark, and finally the analyst forecast benchmark. Although this period

covers years examined by Degeorge et al. [1999], the importance of the earnings levels and

earnings changes benchmarks is reversed. Brown and Caylor [2005] find that for the period

from 1996-2002 the analyst forecast benchmark becomes the most important benchmark,

followed by the earnings changes benchmark, and finally the earnings level benchmark. The

importance of the earnings level benchmark and the earnings changes benchmark has remained

constant over time, based on the slope coefficient from their regression of standardized

difference on year.25

25 As discussed in Section II, Subsections B, Brown and Caylor [2005] also examine the incremental valuation consequences of meeting one benchmark as compared to meeting none, and meeting a third benchmark as compared to having met the other two benchmarks. They find that prior to 1993, it is hard to determine which benchmark has the largest incremental valuation consequence. From 1993-2002 it is clear that meeting or beating the analyst

36

As stated in Section II, Graham et al. [2005, p. 22] survey financial executives from

public companies and ask: How important are the following earnings benchmarks to your

company when you report a quarterly earnings number? Based on responses, Graham et al.

[2005] add yet another hierarchy to the mix, with the earnings changes benchmark being first,

followed by the analyst forecast benchmark, and finally the earnings level benchmark. Graham

et al. [2005, p. 22-23] also perform a similar analysis conditional upon firm characteristics. For

example, they show that large, profitable, public firms that list on the New York Stock Exchange

(NYSE), that have high sales growth, high debt to asset ratios, actively guide analysts, and have

large analyst following are more likely to agree that the analyst consensus forecast is important.

As a caveat, Graham et al. [2005] do not specifically ask CEOs which benchmark is the most

important to them.

More research is needed to examine the earnings benchmark hierarchy conditional on

firm characteristics. Similar to Graham et al. [2005], additional insight may be gained by

examining the hierarchy based on prominent firm characteristics (e.g. small vs. large firms, high

leverage vs. low leverage firms, or high vs. low analyst following). These characteristics would

benefit standard setters in aligning both (1) the motives/incentives for earnings management

around benchmarks and (2) the earnings management methods for beating specific benchmarks

with the firms that are most likely to have that benchmark at the top of their hierarchy. Another

area of research is to determine if there is a disconnect between what CEOs think or report is the

most important benchmarks [i.e. Graham et al., 2005] and which benchmark is actually the most

important [i.e. Brown and Caylor, 2005]

forecast benchmark has the largest incremental valuation consequence, whether you compare meeting one benchmark to meeting none or meeting a third benchmark as compared to meeting the other two benchmarks. Care needs to be taken in interpreting the valuation results, as Brown and Caylor [2005] use firms above and below the benchmark regardless of their proximity to the benchmark (this was discussed previously in Section II, Subsection E).

37

B –Firms below benchmarks

Burgstahler and Dichev [1997, p. 112] examine the earnings level benchmark and

“conjecture that the extent of earnings management is likely to be a function of the ex ante costs

of earnings management. In other words, earnings manipulators are likely to be firms which

faced relatively lower ex ante costs of earnings management. Therefore, given that earnings

manipulators moved from slightly negative earnings to slightly positive earnings, firms with

slightly negative earnings likely are those which faced higher ex ante earnings management costs

than firms with slightly positive earnings.”

As mentioned previously, if firms truly face incentives to beat the three earnings

benchmarks, then why are there any firms just below a benchmark? What keeps firms just below

a benchmark from moving to meet or slightly beat a benchmark? Burgstahler and Dichev [1997]

begin to address these questions. They posit that (1) working capital accruals ostensibly offers

the most readily available means by which earnings can be managed, (2) marginal manipulation

of working capital accruals are more easily ‘buried’ when firms report high levels of current

assets and current liabilities and this reduces the costs of managing earnings, and (3) firms just

above an earnings benchmark offer a fruitful group to search for earnings managers. Burgstahler

and Dichev [2004, p. 114-115] offer limited evidence in support of this idea. In Figure 5 and 6,

they show that firms just above the earnings level benchmark have higher levels of current assets

and current liabilities than firms just below the benchmark. However, they neglect to investigate

whether this condition holds for alternative benchmarks.

Future research may look at constraints that might be keeping firms below a benchmark

from managing earnings and whether firms below a benchmark have the same market sensitivity

to earnings announcements as firms above. As mentioned in Section VI, Barton and Simko

38

[2002] use NOA, a balance sheet measure, as a proxy for a firm’s ability to manage earnings and

find that firms that miss analysts’ forecasts have higher levels of NOA than firms that make the

forecasts. Their study can be extended to the other benchmarks (earnings levels and earnings

changes). Other types of constraints can also be examined. Kasznik [1999] uses lagged change

in total accruals as a measure of firms’ ability to manage earnings in a voluntary management

earnings forecast setting. This measure could also be extended to the earnings benchmarks.

Research is also needed to examine differences in incentives for firms just above and just

below benchmarks. Firms just below the benchmark may not have the same incentives as the

firms above. Earnings response coefficients (ERC) and analyst stock recommendations

[Abarbanell and Lehavy, 2003] are a few potential proxies for firms’ incentives to meet or just

beat benchmarks. Payne and Thomas [2004] examine whether a firm unexpectedly meeting the

earnings levels or earnings changes benchmarks affects the firm’s Returns/Earnings Relation.

They measure earnings expectations using the most recent analyst forecast not within the 20 days

prior to the earnings announcements. They find that there is no ‘special effect’ for unexpectedly

beating the benchmarks. Payne and Thomas [2004] do not examine how earnings management

affects their results. More can be done with the earnings response coefficient (ERC) above and

below benchmarks. Firms just below may not have the same reward for meeting a benchmark as

measured by ERC. Abarbanell and Lehavy [2003] propose analyst stock recommendations as an

alternative to the ERC because of the ‘staleness’ that ERC can contain. Further research into

constraints and incentives can help to answer why there are so many firms just below

benchmarks.

39

C – Industry earnings benchmarks

Beaver et al. [2003] focus on property-casualty insurers that are managing earnings

around the earnings level benchmark. Incentives for insurers suggest that they would also

benefit from meeting or beating the earnings changes benchmark. Beaver et al. [2003] do not

investigate whether the earnings changes benchmark is important to property casualty insurers

and whether the cross-sectional distribution of earnings changes supports the importance. If

results do not hold for this alternative benchmark, it would be interesting to know why.26

Industry earnings standards or norms also affect firms. This may translate into firms not

only trying to meet the three earnings thresholds already mentioned, but also earnings thresholds

set by other firms within the same industry. We also leave industry earnings benchmarks to

future research.

D – Multiple Thresholds

Many of the results presented above examine earnings management around only one

threshold. As in the insurance industry, one benchmark is examined while the other two

benchmarks are not discussed. Sometimes a benchmark is not examined because of the situation

or context. For example, as pointed out in Beaver et al. [2005], examining tax issues around the

earnings level benchmark would likely not be fruitful because of the difference in taxes for profit

and loss firms. However, many of the studies would benefit from examining whether results

hold around the alternative earnings benchmarks.27 If results do not hold, examining what

26 Beatty et al. [2004] only look at the earnings changes benchmark for public and private banks. They note in their research that the reason they do not look at the earnings level benchmark is because there are few banks that report losses. 27 For example, Dhaliwal et al. [2004] focus on the analyst forecast benchmark. and do not examine the earnings level and earnings changes benchmark. As previously mentioned, not examining the earnings level benchmark in tax research is appropriate because taxes or ETRs for loss firms can be confounding. Additional research is needed to see if firms’ management use ETRs to help the firm meet the earnings changes benchmark. If firms are not using ETRs to meet this additional benchmark, it would be interesting to document what firm characteristics may cause results to differ from the analyst forecast benchmark.

40

features of the sample that cause one benchmark to be more important to a firms’ management

will benefit our understanding of the benchmark literature. This will also help to explain sample

features that cause the hierarchy of benchmark importance to change.

More research is also needed on the interaction of the benchmarks. Hansen [2008] finds

that firms missing one benchmark may have high levels of discretionary accruals because they

are trying to meet an alternative benchmark. Brown and Caylor [2005] examine the valuation

consequences of meeting multiple benchmarks as compared to meeting just one or none.

Research is needed to examine whether firms exhibit earnings management behavior that is

consistent with the increased incentives of meeting multiple benchmarks.

E – Consecutive strings of beating benchmarks

Burgstahler and Dichev [1997] examine the distribution of firms with earnings changes.

They show there is a break in the distribution with too few firms just below and too many firms

just above the earnings changes benchmark. Interestingly they note that the result is magnified

when they examine firm-year observations with three or more years of prior earnings increases.

Research previously cited (Section II, Subsection B) in the capital market incentive section [e.g.

Barth et al., 1999; DeAngelo et al., 1996; Kasznik and McNichols, 2002] finds that firms with

consecutive years of beating the earnings changes and analyst forecast benchmarks are rewarded.

More research is needed to see if earnings management plays a part in firms that have had or are

trying to maintain a string of beating benchmarks.

F – Other Possible Benchmarks: Percentage Change in Earnings

In this review we have focused on three earnings benchmarks. There may be other

benchmarks of interest to firm management. For example, when companies put out their

earnings announcement press releases, they often report their changes in EPS as a percentage

41

change. In addition to firms being interested in having an increase in earnings, firm management

may work towards a consistent percentage increase year after year.

<Insert Figure 3 and Table 2>

To emphasize this additional benchmark, we include the distribution of percentage change of Net

Income and EPS to highlight some areas for future research. Figure 3, Panel A contains the

distribution of percentage change in un-scaled Net Income. The actual numbers at each

percentage between 0 and 40% are included in Table 2, Panel A. For Net Income, the break in

the distribution below zero is similar to the break found in Burgstahler and Dichev [1997] and

Durtschi and Easton [2005]. Table 2, Panel A reports the number of firms at each percentage

change that had the same percentage change in the previous year (Column D), a current

percentage change within +/- 1% of the previous year change (Column F), and a current

percentage change within +/- 3% of the previous year change (Column H). The firms in Column

H make up 13% (See Column I) of the total firms from Column B for the 5, 10, 12, and 20

percent change groups, and 16% of the 15 percent change group. Research is needed to see if

earnings management is used to maintain consistent percentage changes in Net Income.

Figure 3, Panel B contains the distribution of percentage change in EPS. Percentage

change in EPS has a larger break in the distribution just below zero. Also there is a large spike

in firms that report a zero percent change in EPS28. Table 2, Panel B also contains the actual

number of firms at each percentage change between 0 and 30% and a few selected percentages29.

Column H averages 8.82% (See Column I) of Column B for the 0 to 30 percentage change

groups. In comparison, Column H averages 3.69% of Column B for the 31 to 60 percentage

28 This number may be overstated as zero percent change includes firms with -0.005 ≤ Percentage Change < 0.005 due to rounding 29 Of interest, Table 2 Panel B contains selected actual numbers for Figure 3, Panel B. There are jumps in the distribution for the +/- 33, +/-50, +/-67, and +/-100 percentage groups.

42

change groups (not reported). Similar to the percentage change in Net Income, research is

needed to see if earnings management is used to maintain consistent percentage changes in EPS.

Also an examination of the valuation consequences of having consistent percentage changes in

Net Income/EPS would help solidify this research. As mentioned previously, there may be other

benchmarks of interest and percentage change in earnings is just one example. Delisting

requirements were another potential benchmark discussed in Section III, Subsection B.

G – Summary and Conclusion

In this review we examine earnings management around three earnings benchmark. We

use the four evidences suggested by Healy and Wahlen [1999] as a framework for this review.

They examine the earnings management literature and posit that standard setters are interested in

evidence to help determine the amount of judgment to allow management to exercise in financial

reporting. The evidences are (1) the magnitude and frequency of any earnings management, (2)

specific accruals and accounting methods used to manage earnings, (3) motives for earnings

management, and (4) and resource allocation effects in the economy. Dechow and Skinner

[2000] highlight earnings benchmarks as a fruitful setting because of the strong capital market

incentive to beat the benchmarks.

In Section II, we start the review by focusing on firms’ management incentives or

motives to beat the earnings benchmarks. Although results are mixed, generally firms’

management has capital market incentives and also compensation incentives to meet the three

earnings benchmarks. Auditors are also aware of firms’ management desire to beat the analyst

forecast benchmark. Research can examine whether auditors are aware of the importance to

clients of beating alternative benchmarks. More research is also needed to distinguish between

43

capital market incentives to beat specific benchmarks versus rewards that come from better

performance (not specifically from the benchmark).

In Section III, we review frequency and magnitude of earnings management around

benchmarks. There have been distribution studies, which highlight the frequency of earnings

management around benchmarks. Recent research has questioned this evidence. Healy and

Wahlen [1999, p. 379] note that distribution research addresses the frequency but not the

magnitude of earnings management around benchmarks. Since Healy and Wahlen [1999], there

has been little research on the magnitude of earnings management. This is in part due to

difficulties in measuring discretionary accruals.

In Section IV, we focus on the methods firms are using to manage earnings around

benchmarks. In the banking industry, management appears to use loan loss reserve and security

gain realizations to help firms meet earnings benchmarks. In the insurance industry,

management appears to use loss reserves to meet earning benchmarks. Outside of regulated

industries, firms’ management appear to use restructuring charges, aggressive revenue

recognition, pro forma earnings, tax expense (ETR), abnormal levels of the valuation allowance

account, rounding EPS, and real activities to help firms meet earnings benchmarks.

Discretionary (abnormal) accruals and deferred tax expense are aggregate measures that appear

to identify firms that beat benchmarks.

In Section V, we discuss resource allocation effects from earnings management around

benchmarks. Research supports that firms receive rewards for meeting the analyst forecast

benchmark, even after managing earnings to do so. In section VI, we discuss factors that limit

benchmark beating behavior. Finally, we discuss ideas for future research and provide (1)

characteristics of firms that miss benchmarks, (2) industry specific benchmarks, (3) research

44

examining multiple benchmarks, (4) firms maintaining consecutive strings of beating

benchmarks, and (5) percentage changes in Net Income/EPS as promising areas of future

research. Evidence is split on which earnings benchmark is the most important for firms’

management and more research is needed to solidify a ‘hierarchy of importance’.

In conclusion, although there are a plethora of proposed methods of earnings

management to beat benchmarks, little has been done to combine measures and/or create

predictive models that standard setters and regulators can use to identify firms that are managing

earnings. Empirical research in the last decade has continued to document statistical differences

in characteristics of firm-observations around earnings benchmarks. This research is descriptive

and interesting, but (as outlined in this review) more needs to be done to help this research be

beneficial to standard setters and regulators.

45

Figure 1 – Graphs of Possible Functions for the Earnings Benchmark – Executive Compensation Relation

Panel A

0

5

10

15

20

25

30

35

-15 -12 -9 -6 -3 0 3 6 9 12 15

Earnings Benchmarks

Ca

sh

Co

mp

en

sa

tio

n

Panel B

0

5

10

15

20

25

30

35

40

45

-15 -12 -9 -6 -3 0 3 6 9 12 15

Earnings Benchmarks

Ca

sh

Co

mp

en

sa

tio

n

46

Figure 2 – Distribution of EPS for firm-year observations from 1988 - 2005

Panel A – All firm-year observations

0

200

400

600

800

1000

1200

1400

1600

1800

-50

-44

-38

-32

-26

-20

-14 -8 -2 4

10

16

22

28

34

40

46

EPS

Fir

m-Y

ear

Ob

serv

ati

on

s

EPS (Cents) # Firms EPS (Cents) # Firms -20 442 20 480

-19 500 19 397

-18 488 18 432

-17 502 17 426

-16 487 16 476

-15 513 15 460

-14 523 14 483

-13 565 13 469

-12 623 12 553

-11 627 11 508

-10 630 10 596

-9 686 9 515

-8 722 8 547

-7 680 7 594

-6 803 6 619

-5 911 5 629

-4 895 4 733

-3 976 3 746

-2 1091 2 908

-1 1268 1 1095

0 1543

47

Panel B – Distribution of EPS for firm-year observations with end-of-the-year stock price ≥ $1.00

0

100

200

300

400

500

600

700

-50 -40 -30 -20 -10 0 10 20 30 40 50

EPS

Fir

m-Y

ear

Ob

serv

ati

on

s

EPS (Cents) # Firms EPS (Cents) # Firms -20 301 20 461

-19 321 19 388

-18 336 18 409

-17 324 17 413

-16 306 16 448

-15 327 15 432

-14 316 14 439

-13 317 13 426

-12 354 12 503

-11 348 11 440

-10 350 10 518

-9 355 9 438

-8 356 8 472

-7 342 7 480

-6 372 6 476

-5 378 5 496

-4 352 4 529

-3 366 3 483

-2 356 2 567

-1 350 1 618

0 466

48

Panel C – Distribution of EPS for firm-year observations with end-of-the-year stock price < $1.00

0

200

400

600

800

1000

1200

-50 -40 -30 -20 -10 0 10 20 30 40 50

Fir

m-Y

ea

r O

bs

erv

ati

on

s

EPS (Cents) # Firms EPS (Cents) # Firms -20 141 20 19

-19 179 19 9

-18 152 18 23

-17 178 17 13

-16 181 16 28

-15 186 15 28

-14 207 14 44

-13 248 13 43

-12 269 12 50

-11 279 11 68

-10 280 10 78

-9 331 9 77

-8 366 8 75

-7 338 7 114

-6 431 6 143

-5 533 5 133

-4 543 4 204

-3 610 3 263

-2 735 2 341

-1 918 1 477

0 1077

49

Panel D – Distribution of EPS for firm-year observations that are close to delisting requirements ($1.50 ≥ end-of-the-year stock price ≥ $1.00)

0

20

40

60

80

100

120

140

-50 -40 -30 -20 -10 0 10 20 30 40 50

EPS

Fir

m-Y

ear

Ob

serv

ati

on

s

EPS (Cents) # Firms EPS (Cents) # Firms -20 51 20 22

-19 58 19 21

-18 52 18 23

-17 58 17 28

-16 45 16 42

-15 54 15 27

-14 50 14 40

-13 60 13 42

-12 77 12 51

-11 69 11 45

-10 63 10 67

-9 66 9 68

-8 74 8 59

-7 60 7 73

-6 76 6 79

-5 76 5 86

-4 76 4 93

-3 67 3 84

-2 65 2 112

-1 73 1 117

0 101

Panel A is the distribution of fully diluted EPS (Compustat Data 57) for the 47,429 firm-year observations with EPS between -$0.50 and +$0.50 in the 2005 Compustat Annual File (For all Panels, EPS between -$0.20 and +$0.20 in the accompanying table). Panel B is the same EPS distribution for the 34,294 firm-year observations from Panel A with end-of-the-year stock price (Compustat Data 199) ≥ $1.00. Panel C is the same EPS distribution for the 13,135 firm-year observations from Panel A with end-of-the-year stock price < $1.00. Panel D is the same EPS distribution for the 3,753 firm-year observations from Panel A that were close to a delisting mechanism ($1.50 ≥ end-of-the-year stock price ≥ $1.00).

50

Figure 3

Panel A – Percentage Change in Net Income for Compustat firm-year observations from 1988 – 2005 with change between +/- 1

0

100

200

300

400

500

-100 -80 -60 -40 -20 0 20 40 60 80 100

Percentage Change in Net Income

Fir

m-Y

ear

Ob

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s

Panel B – Percentage Change in EPS for Compustat firm-year observations from 1988 – 2005 with change between +/- 1

0

100

200

300

400

500

600

700

800

900

-100 -80 -60 -40 -20 0 20 40 60 80 100

Percentage Change in EPS

Fir

m Y

ear

Ob

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on

s

Panel A is the distribution of Percentage Change in Net Income (Compustat Data 172) for the 34,890 firm-year observations with percentage changes between +/-1 from the 2005 Compustat Annual File (The actual numbers at each percentage between 0% and 40% are found in Table 2, Panel A). Panel B is the Percentage Change in EPS (Compustat Data 57) distribution for the 36,234 firm-year observations with percentage changes between +/-1 from the 2005 Compustat Annual File (The actual numbers at each percentage between 0% and 30% and other selected percentages are found in Table 2, Panel B). Percentage Change in Net Income = [(Data 172 in year t) – (Data 172 in year t-1)] ÷ abs(Data 172 in year t-1). Percentage Change is EPS is calculated the same way by replacing Data 172 with Data 57.

51

Table 1 Methods of Earnings Management to Beat Benchmarks

Panel A: Insurance and Banking Industry Authors Benchmark and Method Results

Beaver, McNichols, and

Nelson [2003]

Earnings Levels – Claim Loss Reserve

Public and Mutual property-casualty insurers use claim loss reserve to get from below the earnings level threshold to above.

Beatty, Ke, and Petroni [2002]

Earnings Changes – Loan Loss Provisions and Realized Security Gains and Losses

Publicly held banks are more likely than privately held banks to use loan loss provisions and realized security gains and losses to move from just missing the earnings changes benchmark to just beating.

Panel B: Non-regulated firms – Aggregate accrual measures Authors Benchmark and Method Results

Dechow, Richardson, and

Tuna [2003]

Earnings Levels – Discretionary Accruals (Forward looking model)

No difference in discretionary accrual levels for firms just above and just below earnings level benchmark

Hansen [2008] Earnings Levels and Earnings Changes – Discretionary Accruals (Forward looking model)

Discretionary accrual levels are affected by alternative benchmarks around the earnings level and earnings changes benchmark. After controlling for these alternative benchmarks, discretionary accruals for firms just above the earnings level benchmark are higher than firms just below.

52

Table 1 (Continued) – Methods of Earnings Management to Beat Benchmarks

Panel B: Non-regulated firms – Aggregate accrual measures (Continued)

Authors Benchmark and Method Results

Phillips, Pincus, and Rego [2003] – hereafter PPR

Earnings Levels, Earnings Changes, and Analyst Forecast – Deferred Tax Expense, Total Accruals, Discretionary Accruals (Modified Jones and Forward Looking), and Cash Flows

Firms above the benchmark are identified as earnings managers. Deferred tax expense, discretionary accruals, and total accruals are incrementally useful in classifying firms as earnings managers around the earnings changes benchmarks. Deferred tax expense holds after controlling for performance. Deferred Tax Expense and the accrual measures (total and discretionary) are incrementally useful in classifying earnings managers around the earnings level benchmark. None of the measures are incrementally useful in identifying earnings managers around the earnings forecast benchmark.

Ayers, Jiang, and Yeung [2006]

Earnings Levels, Earnings Changes, and Analyst Forecast – Deferred Tax Expense, Total Accruals, Discretionary Accruals (Modified Jones and Forward Looking), and Cash Flows

Perform tests similar to PPR and extend the tests to see if performance drives the results. The authors examine measures around pseudo-benchmarks (which are not centered on the earnings benchmarks) and expect no more than ten percent of the pseudo benchmarks to be significant if performance is not driving results. For the analyst forecast benchmark, total accruals and discretionary accruals (either model) are significantly higher for earnings managing firms and results do not hold for more than ten percent of the pseudo-benchmarks. For the earnings changes and earnings levels benchmarks, many of the measures are significant, but they are also significant for more than ten percent of the pseudo benchmarks. They do find that results are more pronounced around the actual benchmark.

53

Table 1 (Continued) – Methods of Earnings Management to Beat Benchmarks

Panel C: Non-regulated firms – Specific Activities or Accounts

Authors Benchmarks and Method Results

Das and Zhang [2003] Earnings Levels, Earnings

Changes, and Analyst Forecast – EPS and working capital accruals

Firms round EPS when rounding helps them achieve one of the three earnings benchmarks. Firms that have rounded have higher levels of working capital accruals.

Moehrle [2002] Earnings Levels, Earnings Changes, and Analyst Forecast – Restructuring Charge Reversals

Firms use restructuring charge reversals when pre-reversal earnings fall short of the earnings levels and analyst forecast benchmark. There is some evidence that firms use reversals to meet the earnings changes benchmarks, but results are not as strong as for the other two benchmarks.

Roychowdhury [2006] Earnings Levels – Price Discounts, Overproduction, Discretionary Expense Reduction (Real Earnings Management)

Firms just above the earnings level benchmark (1) offer price discounts to give a short term boost to sales, (2) overproduce to lower their cost of goods sold number, and (3) reduce discretionary expenses (e.g. selling & administrative and research & development) as compared to firms just below.

Marquardt and Wiedman [2004]

Earnings Changes – Special Items Firms above the earnings changes benchmark have higher positive special items than a performance matched control sample.

Altamuro, Beatty, and Weber [2005]

Earnings Levels and Earnings Changes – Revenue Recognition

The authors examine firms that were required to restate earnings as a result of the Security and Exchange Commission (SEC) issuing Staff Accounting Bulletin (SAB) 101 which deals with revenue recognition. They find that firms were using revenue recognition to help them meet the earnings levels and earnings changes benchmark prior to restatement.

Doyle and Soliman [2005]

Analyst Forecast – Pro Forma Earnings

The likelihood of beating the earnings forecast benchmark increases with the use of pro forma earnings. The authors define ‘pro forma use’ as firms that exclude expenses from their pro forma number so pro forma earnings are greater than GAAP earnings

54

Table 1 (Continued) – Methods of Earnings Management to Beat Benchmarks

Panel C: Non-regulated firms – Specific Activities or Accounts (Continued)

Authors Benchmarks and Method Results

Christensen and Black [2007]

Earnings Levels and Analyst Forecast – Pro Forma Earnings

Managers who infrequently make adjustments to pro forma earnings are more likely to use the adjustments to meet the earnings levels and analyst forecast benchmark, as compared to managers who make frequent adjustments. The exclusion of recurring items is indicative of managers’ opportunistic use of pro forma earnings.

Dhaliwal, Gleason, and Mills [2004]

Analyst Forecast – Income Tax Expense

Managers lower their effective tax rate (ETR) in the 4th Quarter when the 3rd Quarter ETR estimate would have caused the firms to miss the analyst forecast benchmark.

Frank and Rego [2006] Earnings Level, Earnings Changes, and Analyst Forecast – Deferred Tax Asset Valuation Allowance Account

Firms use the deferred tax asset valuation allowance account to help them meet the analyst forecast benchmark. The authors use a measure of the abnormal level of the deferred tax asset valuation allowance account.

Panel D: Classification Shifting and Forecast Guidance

Authors Benchmarks and Method Results

McVay [2006] Analyst Forecast – Shifting Core

Expenses to Special Items (Classification Shifting)

Related to pro forma earnings, classification shifting moves expenses from core expenses to special items, although GAAP earnings remain the same. Analysts exclude special items from their forecast of earnings. Managers use classification shifting to meet the analyst forecast benchmark.

55

Table 1 (Continued) – Methods of Earnings Management to Beat Benchmarks Panel D: Classification Shifting and Forecast Guidance (Continued) Authors Benchmarks and Method Results

Athanasakou, Strong, and Walker [2006]

Analyst Forecast – Classification Shifting

Larger firms in the UK just meet or beat the analyst forecast by shifting small core expenses to other non-recurring items instead of using income-increasing abnormal accruals

Burgstahler and Eames [2006]

Analyst Forecast – Forecast Guidance and Discretionary Accruals

The authors find evidence of both upward earnings management (using discretionary accruals) and downward forecast management (Matsumoto 2002, Forecast Guidance Model) to meet and just beat the analyst forecast benchmark.

Lin, Radhakrishnan, and Hu [2006]

Analyst Forecast – Discretionary Accruals, Classification Shifting, and Real Activities Management

Using a comprehensive set of earnings management tools, the authors find that the use of upward discretionary accruals, classification shifting and downward forecast guidance are the primary tools used by managers to meet and or beat analyst forecast benchmark; but not real activities manipulation

Athanasakou, Strong, and Walker [2008]

Analyst Forecast – Discretionary Accruals and Real Activities Management

In a UK context, managers are more likely to use real activities management as opposed to accruals management to achieve the analyst forecast threshold

Brown and Higgins [2005] Analyst Forecast – Discretionary Accruals and Forecast Guidance

Managers use forecast guidance instead of upward earnings management to avoid negative earnings surprises in strong-investor-protection environments. The regulation of forecast guidance in this environment is far less rigorous than that of earnings management. On the contrary, in weak-investor-protection environments, regulation of reported earnings is not stringent, allowing managers to use upward earnings management rather than forecast guidance for meeting or beating benchmarks.

56

Table 2

Panel A – Selected Percentage Change in Net Income for Compustat firm-year observations from 1988 – 2005 (Corresponds to Figure 3, Panel A)

(a)

% Change

(b)

N

(c) %

Change > than prior year

(d)

% Change = prior

year

(e)

(d)÷(b)

(f)

% Change within +/-

1% of prior year

(g)

(e)÷(b)

(h) %

Change within +/-

3% of prior year

(i)

(f)÷(b) 0 254 87 4 1.6% 10 3.9% 18 7.1%

1 305 119 4 1.3% 9 3.0% 23 7.5%

2 305 116 0 0.0% 4 1.3% 16 5.2%

3 303 117 4 1.3% 15 5.0% 29 9.6%

4 310 122 4 1.3% 8 2.6% 26 8.4%

5 329 133 5 1.5% 13 4.0% 45 13.7%

6 310 127 3 1.0% 15 4.8% 32 10.3%

7 351 146 12 3.4% 18 5.1% 35 10.0%

8 314 118 5 1.6% 12 3.8% 35 11.1%

9 310 125 3 1.0% 12 3.9% 31 10.0%

10 327 126 5 1.5% 17 5.2% 45 13.8%

11 346 165 10 2.9% 22 6.4% 40 11.6%

12 338 170 5 1.5% 18 5.3% 46 13.6%

13 352 169 9 2.6% 22 6.3% 39 11.1%

14 331 158 8 2.4% 18 5.4% 33 10.0%

15 405 198 13 3.2% 31 7.7% 65 16.0%

16 318 140 4 1.3% 19 6.0% 38 11.9%

17 366 168 9 2.5% 17 4.6% 41 11.2%

18 317 164 3 0.9% 15 4.7% 33 10.4%

19 324 145 6 1.9% 14 4.3% 27 8.3%

20 327 158 9 2.8% 24 7.3% 43 13.1%

21 330 171 3 0.9% 14 4.2% 28 8.5%

22 314 167 1 0.3% 9 2.9% 22 7.0%

23 313 172 8 2.6% 19 6.1% 36 11.5%

24 321 170 0 0.0% 9 2.8% 22 6.9%

25 312 170 8 2.6% 17 5.4% 30 9.6%

26 336 179 3 0.9% 14 4.2% 38 11.3%

27 290 160 3 1.0% 11 3.8% 20 6.9%

28 270 153 5 1.9% 12 4.4% 20 7.4%

29 251 133 5 2.0% 9 3.6% 14 5.6%

30 235 131 0 0.0% 4 1.7% 13 5.5%

31 269 157 2 0.7% 7 2.6% 22 8.2%

32 267 164 1 0.4% 10 3.7% 22 8.2%

33 255 154 3 1.2% 10 3.9% 19 7.5%

34 248 148 3 1.2% 5 2.0% 15 6.0%

35 228 152 3 1.3% 8 3.5% 19 8.3%

36 229 132 4 1.7% 7 3.1% 12 5.2%

37 254 153 3 1.2% 7 2.8% 14 5.5%

38 206 127 2 1.0% 3 1.5% 8 3.9%

39 260 165 1 0.4% 2 0.8% 10 3.8%

40 212 139 1 0.5% 3 1.4% 11 5.2%

57

Panel B – Selected Percentage Change in EPS for Compustat firm-year observations from 1988 – 2005 (Corresponds to Figure 3, Panel B)

(a)

% Change

(b)

N

(c) %

Change > than prior year

(d)

% Change = prior

year

(e)

(d)÷(b)

(f)

% Change within +/-

1% of prior year

(g)

(e)÷(b)

(h) %

Change within +/-

3% of prior year

(i)

(f)÷(b) -100 275 - - - - - - -

-75 135 - - - - - - -

-67 183 - - - - - - -

-50 241 - - - - - - -

-40 221 - - - - - - -

-33 280 - - - - - - -

-20 272 - - - - - - -

0 768 340 49 6.4% 55 7.2% 71 9.2%

1 256 100 6 2.3% 8 3.1% 18 7.0%

2 282 120 2 0.7% 6 2.1% 12 4.3%

3 308 138 5 1.6% 14 4.5% 27 8.8%

4 313 138 5 1.6% 16 5.1% 31 9.9%

5 344 143 5 1.5% 12 3.5% 34 9.9%

6 303 139 5 1.7% 16 5.3% 30 9.9%

7 304 135 5 1.6% 15 4.9% 34 11.2%

8 314 141 4 1.3% 11 3.5% 25 8.0%

9 314 158 0 0.0% 11 3.5% 33 10.5%

10 349 169 4 1.1% 13 3.7% 31 8.9%

11 380 202 6 1.6% 22 5.8% 36 9.5%

12 365 186 9 2.5% 19 5.2% 43 11.8%

13 288 156 8 2.8% 19 6.6% 42 14.6%

14 326 182 3 0.9% 8 2.5% 27 8.3%

15 403 236 14 3.5% 34 8.4% 54 13.4%

16 340 192 2 0.6% 20 5.9% 30 8.8%

17 354 203 4 1.1% 13 3.7% 39 11.0%

18 337 193 3 0.9% 11 3.3% 32 9.5%

19 310 179 6 1.9% 15 4.8% 30 9.7%

20 377 228 4 1.1% 16 4.2% 29 7.7%

21 307 178 4 1.3% 15 4.9% 28 9.1%

22 322 205 1 0.3% 3 0.9% 19 5.9%

23 277 181 2 0.7% 11 4.0% 25 9.0%

24 283 182 5 1.8% 15 5.3% 27 9.5%

25 377 224 3 0.8% 9 2.4% 16 4.2%

26 281 171 3 1.1% 9 3.2% 16 5.7%

27 281 170 1 0.4% 5 1.8% 20 7.1%

28 237 154 4 1.7% 11 4.6% 19 8.0%

29 283 185 3 1.1% 7 2.5% 18 6.4%

30 266 178 1 0.4% 6 2.3% 18 6.8%

33 304 183 3 1.0% 5 1.6% 9 3.0%

50 322 210 1 0.3% 2 0.6% 4 1.2%

67 201 152 2 1.0% 3 1.5% 5 2.5%

100 435 356 - - - - - -

58

Percentage Changes correspond with Figure 3. Columns (c), (d), (f), and (h) contains the firms from column (b) that had a higher percentage change in the current year as compared to the prior year, the same percentage change in the current year as in the prior year, a current change within +/- 1% of the prior year, and a current change within +/- 3% of the prior year, respectively. Columns (c), (d), (f), and (h) were not calculated for negative firms. Columns (f) and (h) were not calculated for 100% changes because we did not include firm-year observation above 100% in the analysis.

59

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